Paper Info
Title
Analysis And Classification For Single-Trial Eeg Induced By Sequential Finger Movements
Abstract
In recent years, motor imagery-based BCIs (MI-BCIs) controlled various external devices successfully, which have great potential in neurological rehabilitation. In this paper, we designed a paradigm of sequential finger movements and utilized spatial filters for feature extraction to classify single-trial electroencephalography (EEG) induced by finger movements of left and right hand. Ten healthy subjects participated the experiment. The analysis of EEG patterns showed significant contralateral dominance. We investigated how data length affected the classification accuracy. The classification accuracy was improved with the increase of the keystrokes in one trial, and the results were 87.42%, 91.21%, 93.08% and 93.59% corresponding to single keystroke, two keystrokes, three keystrokes and four keystrokes. This study would be helpful to improve the decoding efficiency and optimize the encoding method of motor-related EEG information.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8857117
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Keywords
Field
DocType
EEG, Sequential Finger Movements, Spatial Filters, BCI
Computer vision,Task analysis,Computer science,Keystroke logging,Speech recognition,Feature extraction,Time–frequency analysis,Artificial intelligence,Decoding methods,Electroencephalography,Motor imagery,Encoding (memory)
Conference
Volume
ISSN
Citations 
2019
1557-170X
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Shan-Shan Zhang1251.84
Kun Wang222.07
Minpeng Xu32717.17
Zhongpeng Wang402.70
Long Chen500.34
Faqi Wang600.34
Lixin Zhang723.75
Dong Ming810551.47